A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909029409751040 |
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| author | Liu, Tianyu Zheng, Wangjie Yang, Rui Loo, Benny Kai Guo Zhang, Hui Lauran, Jeffries Gu, Jianlei Yu, Botao Xuan, Weihao Huang, Kexin Liu, Nan Zou, James Jiang, Yonghui Xu, Hua Zhao, Hongyu |
| author_facet | Liu, Tianyu Zheng, Wangjie Yang, Rui Loo, Benny Kai Guo Zhang, Hui Lauran, Jeffries Gu, Jianlei Yu, Botao Xuan, Weihao Huang, Kexin Liu, Nan Zou, James Jiang, Yonghui Xu, Hua Zhao, Hongyu |
| contents | Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workflows often lead to prolonged assessment times and low accuracy. To address these limitations, we introduce Hygieia, a multi-modal AI agent system designed to support precision disease diagnosis by integrating diverse data sources, including phenotypic features, genetic profiles, and clinical records. Hygieia features a router-based and knowledge-enhanced framework that mitigates hallucination and tailors diagnostic strategies to different disease categories. Notably, it prioritizes risk-related genomic factors for rare diseases and provides confidence scores to assist clinical decision-making. We conducted a comprehensive evaluation demonstrating that Hygieia achieves state-of-the-art performance across multiple diagnostic benchmarks. In collaboration with clinical experts from Yale School of Medicine and Duke-NUS Medical School, we further validated its practical utility by showing (1) Hygieia's superior diagnostic performance compared to physicians with an improvement from 12%-60% and (2) its effectiveness in assisting clinicians with medical records for handling real-world cases. Our findings indicate that Hygieia not only enhances diagnostic accuracy and interpretability but also significantly reduces clinician workload, highlighting its potential as a valuable tool in clinical decision support systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06226 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization Liu, Tianyu Zheng, Wangjie Yang, Rui Loo, Benny Kai Guo Zhang, Hui Lauran, Jeffries Gu, Jianlei Yu, Botao Xuan, Weihao Huang, Kexin Liu, Nan Zou, James Jiang, Yonghui Xu, Hua Zhao, Hongyu Artificial Intelligence Genomics Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workflows often lead to prolonged assessment times and low accuracy. To address these limitations, we introduce Hygieia, a multi-modal AI agent system designed to support precision disease diagnosis by integrating diverse data sources, including phenotypic features, genetic profiles, and clinical records. Hygieia features a router-based and knowledge-enhanced framework that mitigates hallucination and tailors diagnostic strategies to different disease categories. Notably, it prioritizes risk-related genomic factors for rare diseases and provides confidence scores to assist clinical decision-making. We conducted a comprehensive evaluation demonstrating that Hygieia achieves state-of-the-art performance across multiple diagnostic benchmarks. In collaboration with clinical experts from Yale School of Medicine and Duke-NUS Medical School, we further validated its practical utility by showing (1) Hygieia's superior diagnostic performance compared to physicians with an improvement from 12%-60% and (2) its effectiveness in assisting clinicians with medical records for handling real-world cases. Our findings indicate that Hygieia not only enhances diagnostic accuracy and interpretability but also significantly reduces clinician workload, highlighting its potential as a valuable tool in clinical decision support systems. |
| title | A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization |
| topic | Artificial Intelligence Genomics |
| url | https://arxiv.org/abs/2605.06226 |